Product card generation, labeling, inspection, and/or authentication system

Inactive Publication Date: 2005-04-14

FETISOV IGOR V

2 Cites 13 Cited by

AI-Extracted Technical Summary

Problems solved by technology

The production and sale of illegal products causes huge commercial losses.

Furthermore, counterfeit products, which are often made from haphazard raw materials that produce inferior and possibly dangerous products, discredit the trademark of honest manufacturers and create a potential threat to human life and health.

However, those measures are merely external attributes that are not confirmed by their unambiguous and convincing correlation with the qualitative/quantitative characteristics of the product being protected or with the unique technology used by a specific manufacturer [Barcodes EAN/UPC: Aut...

Method used

Such a card is an intrinsic supplement to the existing bar-coding technologies that provide standard methods of addressing the electronic databases corresponding to the specific inspected product. On the other hand, the analytical data of the optical image that are on the product's container and/or in the product documentation and in the corresponding database, improve the protection of both the o...

Benefits of technology

It is another object of the present invention to create a process of card generation and product labeling that will provide a rapid method for reagent-free inspection and authentication of products at all steps of their sale.

It is another object of the present invention to create a highly effective system for protect...

Abstract

Process and device for card generation and labeling for extracted or manufactured consumer products, which enables nondestructive contactless product inspection and/or authentication, including card generation and inspection of packaged products without damage to containers, identification of trade specimens of a product made by a specific manufacturer at every step of its distribution, and detection of counterfeit products and/or detection of product taint or contamination. The process uses the apparatus and methods of spectral analysis to form characteristic multidimensional optical images (signatures), fingerprints, and/or data profiles. Such images are presented as a digital, magnetic, graphic, holographic or other code that can be read by special devices and/or by visual comparison. The code is listed in the product documentation and/or printed directly on the product's container, which is made of a material transparent in the spectral band of the probing and analyzed optical radiation or is equipped with a device transparent in the spectral band of the probing and analyzed optical radiation. The device enables the probing optical radiation to enter and the analyzed optical radiation, which is used to obtain the characteristic optical image (signature) of the packaged product, to exit, and permits comparison of the image with the labeling code or card listed in the product documentation and/or printed directly on the product's container.

Application Domain

Radiation pyrometryPaper-money testing devices +7

Technology Topic

Authentication systemFingerprint +13

Image

Examples

Experimental program(5)

Example

EXAMPLE 1

In order to demonstrate the sensitivity of the proposed method to variations in the micro-component composition of the products, FIGS. 1 and 2 show graphic presentations of the generalized optical images of various products of natural and industrial origin.

FIG. 1 shows graphic presentations of the generalized optical images as a 3D image (“portrait”) and a “fingerprint,” which is a topographical cross-section (a horizontal projection of the family of equidistant cross-sections of the 3D image) for two samples of mineral water of the same type supplied by different manufacturers. As can be seen from the figure, the specimens have generally similar 3D images but, at the same time, they drastically differ in details, which is especially noticeable in graphic images presented as “fingerprints.”

FIG. 2 shows “fingerprints” for various type of industrial products: an alcohol product (vodka) of two identical brands produced by different manufacturers and automotive gasolines of various brands. The figure clearly shows not only the difference between optical images for different brands of homonymous products but also a drastic difference between optical images for one and the same brand of same-type products supplied by different manufacturers.

Example

EXAMPLE 2

In order to demonstrate the average experimental values of the level of agreement and magnitude of error for the characteristic generalized optical images obtained for individual commercial lots of various goods, the following products were selected: 1)—brand 1 automotive gasoline; 2)—brand 2 automotive gasoline; 3)—mineral water; 4)—alcoholic beverage (vodka); 5)—bakery yeast (powder); 6)—shampoo (gel); 7)—dish detergent (gel); 8)—powdered laundry detergent.

Analyses were conducted for series consisting of 5-10 specimens of each type of the above products manufactured in one and the same technological cycle. The results of those analyses are presented in Table 1. As that table shows, the magnitude of error δ was several percentage points and practically all of it was determined by the experimental error of measuring the characteristic intensities TABLE 1 Category of product Gasoline Foodstuffs Chemicals 1 2 3 4 5 6 7 8 A 0.96 0.95 0.95 0.98 0.94 0.97 0.98 0.94 ±δ 0.04 0.05 0.05 0.02 0.06 0.03 0.02 0.06

Example

EXAMPLE 3

In order to demonstrate the levels of agreement for the optical images of homonymous products from an individual manufacturer, three specimens of shampoo of the same brand were sampled; the shampoos were produced in various technological cycles with a time interval of several months. The results of comparison are presented in Table 2. As can be seen from that table, only insignificant variations in the level of agreement due to random measurement errors were observed in products made of identical raw materials in one and the same technological cycle. On the other hand, the level of agreement was substantially lower in specimens from different technological lots. TABLE 2 Lot 1 Lot 2 1-1 1-2 1-3 2-1 2-2 2-3 Lot 1 1-1 1 0.97 0.95 0.82 0.79 0.82 1-2 0.97 1 0.98 0.86 0.85 0.8 1-3 0.95 0.98 1 0.88 0.82 0.85 Lot 2 2-1 0.82 0.86 0.88 1 0.94 0.99 2-2 0.79 0.85 0.82 0.94 1 0.96 2-3 0.82 0.8 0.85 0.99 0.96 1

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Description & Claims & Application Information

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Classification and recommendation of technical efficacy words

highly effective

Product card generation, labeling, inspection, and/or authentication system

AI-Extracted Technical Summary

Problems solved by technology

Method used

Benefits of technology

Abstract

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Examples

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Description & Claims & Application Information

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